Abstract

Malaria is disease which is affecting millions of people and it is generally detected by examining the Red Blood Corpuscles (RBC) manually using microscope. However, the manual microscopic approach is time consuming, and lack of experts in the rural area, makes diagnosis of malaria very challenging one. The reported image processing approch extent the modern digital facilities to address the demand of automation, by developing a computerised facility for the detection of malaria using image processing technique. And this technological development could be a significant part of a modern digital telepathology. Proposed technology helps diagnose through the digital slide. Here the screening of microscopic images of a blood sample is achieved with color image processing approach that involves Red blood corpuscles (RBC) Segmentation, color space conversion, segmentation of the parasite, feature extraction and classification of malarial sample. The presented work detects plasmodium parasites from leishman stained microscopic blood images which in turn support pathologists for faster diagnosis. Neural network and rule based classifiers were used for the classification of blood images. The images belonging to malarial and non-malarial classes.

Keywords

Malaria, RBC, Color space, Plasmodium parasite, Neural network.

Introduction

Malaria is a public health problem and is a tropical disease
which affects millions of people worldwide. The parasite
named Plasmodium causes the life threatening disease malaria.
In 2016, there were an estimated 216 million cases of malaria
in 91 countries [1]. The female anopheles mosquito is
responsible for the transmission of the disease. The malarial
parasites multiply in the liver and infect Red Blood Corpuscles
(RBC) present in the blood.

Malaria diagnosis involves parasite identification or antigens in
the blood of a patient. The blood test can confirm the presence
of malaria and its species. The tests are divided into
microscopic tests and non-microscopic tests. Clinical diagnosis
is dependent on symptoms of the patient and also based on the
physical findings during examination. The initial symptoms of
malaria include headache, chills, fever, sweat, muscle pain,
vomiting and diarrhoea. In severe cases the clinical features
may include anaemia, splenomegaly, hypoglycaemia,
thrombocyte penia, renal dysfunction, and neurologic changes
[2]. The symptoms are also found in some other diseases like
flu and common viral infections. In severe malaria, clinical
findings are prominent and the level of suspicion for malaria
may increase. The Clinical findings must always be confirmed
by a laboratory test [1,2].

Identification of malarial parasites in a blood sample using
microscope is considered as a gold standard for laboratory
confirmation of malaria. However, the malaria screening
manually whole day is a tedious process. Even these analysis
are time consuming and leads to inaccuracy and inconsistency
in some cases. The computerised approach uses digitized blood
slides that will be able to improve the consistence in diagnosis
[3-9].

Reported the computerised automatic approach for detecting
malarial parasite called Plasmodium vivax, by initially
segmenting RBCs. The proposed algorithm is uses an image
processing approaches for classification of images of the smear
into normal and malarial. Thus it helps in upgrading the
existing Lab technology into digital and even helps in
automation.

Methodology

The Leishman stained thin blood smear slides were acquired
with the fluorescence microscope, Olympus (BX51) at
different magnification. The images recorded with the
magnification of 1000X are indicates parasite clearly. The
Olympus DP25 digital camera of 5 MP attached to the light
microscope Olympus BX51, which is connected with the
computer, along with the user interface software (DP2 BSW)
are shown in Figure1. The acquired the blood image from the focused slide area are collected. The typical malarial thin blood
smear image acquired at 40X magnification is shown in the Figure 2. Blood images acquired with the various
magnifications such as 100X, 200X, 400X and 1000X are
shown respectively in the Figures 3A-3d. A total of 143 images
were considered for classification of malarial and non-malarial
classes.

The image processing tool developed helps in the detection of
malaria. The image is acquired from a thin blood slide using a
microscope is processed to eliminate unwanted objects
including platelets. The RBCs are segmented by thresholding
the green channel of RGB image, later the malarial parasites
are detected using saturation image of HSV colour space. The
features are extracted from the gray scale image and parasite
segmented image. The rule-based classifier and artificial neural
network classifiers are used to classify the blood images into
malarial or non-malarial. The approach for malarial
identification using image processing technique is shown in the figure 4.

Figure 4: Classification based on the microscopic image of the blood
smear.

RBC segmentation

The acquired thin blood smear image has red blood corpuscles
(RBC), malarial parasites, Platelets and other objects. But the
proposed technique focus on diagnosis of malaria is based on examination of RBCs, since the malarial parasite infects the
RBC. Thus the region of RBC is analysed with the Red, Green,
Blue components of RGB image. The Green component of the
RGB image has high contrast in order to distinguish
background and foreground. In green layer of the RGB image
the RBCs and the background are visible. With an appropriate
threshold value RBC, the region of interest is extracted. Here
foreground refers to the RBCs [3,5].

Then the binary image is super imposed with the original
image so that the RGB image of extracted Red Blood
Corpuscles in obtained. The normalized RGB image later
converted into different color spaces, such as YIQ, YCbCr,
CMY, LAB and HSV to identify the most suitable space for
segmentation of malarial parasites.

Detection of parasite

Studied the Hue-Saturation-Value (HSV) color space with the
three components of HSV color space namely Hue, Saturation
and Value and are analysed. Better signature of the parasite is
seen in the saturation component of HSV image. The pixel
values of parasite in saturation image are inspected. The
parasite components have higher intensity value than the other
objects in the image. Thus on the experimental basis, the
threshold has been fixed to separate parasite from background.

But that resulted in the segmentation of RBC with the minor
noise components which resemble the parasite. Hence
morphological techniques are used to remove such minor
unwanted objects.

Feature extraction

The classification of image involves the extraction of features
and they include statistical, textural features, and geometrical
features. The statistical features including mean, variance and
standard deviation of a gray scale images are calculated. A
gray level co–occurrence matrix (GLCM) is created from the
grayscale image. Features are extracted from co-occurrence
matrix to reduce space dimensionality. Geometrical feature
such as area is calculated from a parasite segmented image [5].

Feature classification

The extracted features are used to classify the blood slides into
non-malarial and malaria classes, using neural network
classifier. Here the feed forward artificial neural networks are
initially trained, the tested and evaluated. The training phase of
an artificial neural network involves, providing the network
with input feature vector and their respective target vectors
(that is the desired outputs). The neural network classifier is
designed for classification of blood images into malarial or
non-malarial. The MATLAB is used for developing the
proposed image processing approach, for identification of the
malarial parasite from thin blood microscopic image.

Results

The microscopic blood image consisting of trophozoite stage
Plasmodium vivax parasite is considered for the detection of
malaria. The acquired images are in RGB image format and a
typical blood image with Plasmodium vivax is shown in the Figure 5a. The red, green, and blue components that are extracted from the RGB image and are hown in the Figure
5b-5d. Each of the given color image components are analysed.

Figure 5: The colour components of the RGB image: (a) Input RGB
image; (b) The red colour component of the RGB image; (c) The
green colour component of the RGB image; (d) The blue colour
component of the RGB image.

The green colour component has shown more details of RBC.
That even gives a better contrast details between the
background and foreground. Green component image is used
for segmentation of the RBCs. The binary image obtained an
appropriate threshold and given in the Figures 6a and 6b. This
binary image generally contains platelet and other objects. The
unwanted objects are removed using an area based threshold
approach, the results are shown in Figure 6c. Because of the
factors such as staining involved with blood slide the
preparation and condition of the light during image acquisition
still remind still challenging. To overcome this, the segmented images are normalised and the image is shown in the Figure
6d.

The different colour spaces are initially studied for the
extraction of the parasite and finally the HSV colour space has
shown better presence of parasite. The normalized RGB image
is converted into HSV colour space and is shown in the Figure
7a. The hue, saturation and value components are extracted
from the HSV image and are shown in the Figures 7b-7d respectively.

The saturation component of HSV colour space showed better
contrast between background and the parasite region. Using a
threshold technique on the saturation image, the parasite is
segmented and is shown in the figure 8 (a). This image is then
converted into binary and the noise components from the binary image are eliminated using morphological operations
and its colour image obtained is shown in the figure 8 (b).

The classification involves classifying the given input image
into malarial and nonmalarial. The GUI has been used to
display the results of developed malaria identification
algorithm. The GUI is developed to show the intermediate
stages such as loading an image from a folder, RBC
segmentation stage, parasite segmentation stage and finally
classifying the parasite into non-malarial or malaria. The
results obtained from the designed GUI are shown in the Figure 9.

Figure 9: GUI showing plasmodium parasite.

The neural network classifier is fed with geometrical, statistical
and textural features for the classification of microscopic blood
images into malarial or non-malaria. The neural network is fed
with the developed feature vector, which includes mean of the
gray scale image, area of the segmented parasite, contrast,
correlation, energy and homogeneity. The feature vector
consisting of 6 features of 143 samples were used as an input
to the neural network. The accuracies obtained for input feature
vectors are tabulated in Table 1.

Conclusion

The developed algorithm for identifying malarial parasites
from the microscopic images of the blood sample has given
interesting result. This computer based approach is faster and
helps in consistent diagnosis. The developed algorithm
removes the platelets and other smaller components from the
microscopic blood image. The Red Blood Corpuscles (RBC) is
segmented to detect parasite region. Among the different color
spaces studied, the HSV color space is selected, for
segmentation of RBC and parasite. The segmentation of
parasite is performed using saturation image component in
order to detect malarial class. The developed classification tool
is promising with an average accuracy of 96.7%. The
developed algorithm is a promising one could be used in the
rural areas for screening the malarial patients or as part of
digital tele-pathology application.

Acknowledgement

Authors would like to thanks the Haematology Lab, Kasturba
Medical College (KMC), Manipal Academy of Higher Education (MAHE), Manipal providing the facility for
collecting the data. Grateful thanks to Dr. Chethan Manohar,
Professor, Department of Hemetology, K.M.C, MAHE,
Manipal for valuable suggestions. Also they extend
acknowledgements to the department of Biomedical
Engineering, Manipal Institute of Technology (MIT), MAHE,
Manipal.